C++利用opencv实现人脸检测

小编所有的帖子都是基于unbuntu系统的,当然稍作修改同样试用于windows的,经过小编的绞尽脑汁,把刚刚发的那篇python 实现人脸和眼睛的检测的程序用C++ 实现了,当然,也参考了不少大神的博客,下面我们就一起来看看:

Linux系统下安装opencv我就再乱淮危乐褂行┤嗣挥邪沧懊坏魇猿隼磁缧”嗟某绦蚴歉隹樱
sudo apt-get install libcv-dev
sudo apt-get install libopencv-dev
看看你的usr/share/opencv/haarcascades目录下有没有出现几个训练集.XML文件,接下来我拿人脸和眼睛检测作为实例玩一下,程序如下:

好多人不会编译opencv,我再多写几句解决一下好多菜鸟的困难吧

copy完代码之后,保存为xiaorun.cpp哦,记得编译试用个g++ -o xiaorun ./xiaorun.cpp -lopencv_highgui -lopenc_imgproc -lopencv_core -lopencv_objdetect

即可实现

#include <opencv2/highgui/highgui.hpp>
#include <opencv2/imgproc/imgproc.hpp>
#include <opencv2/core/core.hpp>
#include <opencv2/objdetect/objdetect.hpp>
#include <iostream>
using namespace cv;
using namespace std;

void detectAndDraw( Mat& img, CascadeClassifier& cascade,
          CascadeClassifier& nestedCascade,
          double scale, bool tryflip );

int main()
{
  CascadeClassifier cascade, nestedCascade;
  bool stop = false;
  cascade.load("/usr/share/opencv/haarcascades/haarcascade_frontalface_alt.xml");
  nestedCascade.load("/usr/share/opencv/haarcascades/haarcascade_eye.xml");
  // frame = imread("renlian.jpg");
  VideoCapture cap(0);  //打开默认摄像头
  if(!cap.isOpened())
  {
    return -1;
  }
  Mat frame;
  Mat edges;
while(!stop)
{
cap>>frame;
 detectAndDraw( frame, cascade, nestedCascade,2,0 );
 if(waitKey(30) >=0)
 stop = true;
 imshow("cam",frame);
}
  //CascadeClassifier cascade, nestedCascade;
  // bool stop = false;
  //训练好的文件名称,放置在可执行文件同目录下
  // cascade.load("/usr/share/opencv/haarcascades/haarcascade_frontalface_alt.xml");
//  nestedCascade.load("/usr/share/opencv/haarcascades/aarcascade_eye.xml");
//  frame = imread("renlian.jpg");
//  detectAndDraw( frame, cascade, nestedCascade,2,0 );
  // waitKey();
  //while(!stop)
  //{
  //  cap>>frame;
  //  detectAndDraw( frame, cascade, nestedCascade,2,0 );
    if(waitKey(30) >=0)
   stop = true;
  //}
  return 0;
}
void detectAndDraw( Mat& img, CascadeClassifier& cascade,
          CascadeClassifier& nestedCascade,
          double scale, bool tryflip )
{
  int i = 0;
  double t = 0;
  //建立用于存放人脸的向量容器
  vector<Rect> faces, faces2;
  //定义一些颜色,用来标示不同的人脸
  const static Scalar colors[] = {
    CV_RGB(0,0,255),
    CV_RGB(0,128,255),
    CV_RGB(0,255,255),
    CV_RGB(0,255,0),
    CV_RGB(255,128,0),
    CV_RGB(255,255,0),
    CV_RGB(255,0,0),
    CV_RGB(255,0,255)} ;
  //建立缩小的图片,加快检测速度
  //nt cvRound (double value) 对一个double型的数进行四舍五入,并返回一个整型数!
  Mat gray, smallImg( cvRound (img.rows/scale), cvRound(img.cols/scale), CV_8UC1 );
  //转成灰度图像,Harr特征基于灰度图
  cvtColor( img, gray, CV_BGR2GRAY );
  // imshow("灰度",gray);
  //改变图像大小,使用双线性差值
  resize( gray, smallImg, smallImg.size(), 0, 0, INTER_LINEAR );
 // imshow("缩小尺寸",smallImg);
  //变换后的图像进行直方图均值化处理
  equalizeHist( smallImg, smallImg );
  //imshow("直方图均值处理",smallImg);
  //程序开始和结束插入此函数获取时间,经过计算求得算法执行时间
  t = (double)cvGetTickCount();
  //检测人脸
  //detectMultiScale函数中smallImg表示的是要检测的输入图像为smallImg,faces表示检测到的人脸目标序列,1.1表示
  //每次图像尺寸减小的比例为1.1,2表示每一个目标至少要被检测到3次才算是真的目标(因为周围的像素和不同的窗口大
  //小都可以检测到人脸),CV_HAAR_SCALE_IMAGE表示不是缩放分类器来检测,而是缩放图像,Size(30, 30)为目标的
  //最小最大尺寸
  cascade.detectMultiScale( smallImg, faces,
    1.1, 2, 0
    //|CV_HAAR_FIND_BIGGEST_OBJECT
    //|CV_HAAR_DO_ROUGH_SEARCH
    |CV_HAAR_SCALE_IMAGE
    ,Size(30, 30));
  //如果使能,翻转图像继续检测
  if( tryflip )
  {
    flip(smallImg, smallImg, 1);
  //  imshow("反转图像",smallImg);
    cascade.detectMultiScale( smallImg, faces2,
      1.1, 2, 0
      //|CV_HAAR_FIND_BIGGEST_OBJECT
      //|CV_HAAR_DO_ROUGH_SEARCH
      |CV_HAAR_SCALE_IMAGE
      ,Size(30, 30) );
    for( vector<Rect>::const_iterator r = faces2.begin(); r != faces2.end(); r++ )
    {
      faces.push_back(Rect(smallImg.cols - r->x - r->width, r->y, r->width, r->height));
    }
  }
  t = (double)cvGetTickCount() - t;
  //  qDebug( "detection time = %g ms\n", t/((double)cvGetTickFrequency()*1000.) );
  for( vector<Rect>::const_iterator r = faces.begin(); r != faces.end(); r++, i++ )
  {
    Mat smallImgROI;
    vector<Rect> nestedObjects;
    Point center;
    Scalar color = colors[i%8];
    int radius;

    double aspect_ratio = (double)r->width/r->height;
    if( 0.75 < aspect_ratio && aspect_ratio < 1.3 )
    {
      //标示人脸时在缩小之前的图像上标示,所以这里根据缩放比例换算回去
      center.x = cvRound((r->x + r->width*0.5)*scale);
      center.y = cvRound((r->y + r->height*0.5)*scale);
      radius = cvRound((r->width + r->height)*0.25*scale);
      circle( img, center, radius, color, 3, 8, 0 );
    }
    else
      rectangle( img, cvPoint(cvRound(r->x*scale), cvRound(r->y*scale)),
      cvPoint(cvRound((r->x + r->width-1)*scale), cvRound((r->y + r->height-1)*scale)),
      color, 3, 8, 0);
    if( nestedCascade.empty() )
      continue;
    smallImgROI = smallImg(*r);
    //同样方法检测人眼
    nestedCascade.detectMultiScale( smallImgROI, nestedObjects,
      1.1, 2, 0
      //|CV_HAAR_FIND_BIGGEST_OBJECT
      //|CV_HAAR_DO_ROUGH_SEARCH
      //|CV_HAAR_DO_CANNY_PRUNING
      |CV_HAAR_SCALE_IMAGE
      ,Size(30, 30) );
    for( vector<Rect>::const_iterator nr = nestedObjects.begin(); nr != nestedObjects.end(); nr++ )
    {
      center.x = cvRound((r->x + nr->x + nr->width*0.5)*scale);
      center.y = cvRound((r->y + nr->y + nr->height*0.5)*scale);
      radius = cvRound((nr->width + nr->height)*0.25*scale);
      circle( img, center, radius, color, 3, 8, 0 );
    }
  }
  // imshow( "识别结果", img );
}

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